import math

import torch.nn as nn

# if torch.cuda.device_count() > 1:
#     BatchNorm2d = nn.SyncBatchNorm
# else:
#     BatchNorm2d = nn.BatchNorm2d

    
def conv1x1(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        # self.bn1 = BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes, stride)
        # self.bn2 = BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        # out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        # out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, strides=[2,1,2,1,1]):
        self.inplanes = 32
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1,
                               bias=False)
        # self.bn1 = BatchNorm2d(32)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(block, 32, layers[0], stride=strides[0])
        self.layer2 = self._make_layer(block, 64, layers[1], stride=strides[1])
        self.layer3 = self._make_layer(block, 128, layers[2], stride=strides[2])
        self.layer4 = self._make_layer(block, 256, layers[3], stride=strides[3])
        self.layer5 = self._make_layer(block, 512, layers[4], stride=strides[4])

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            # elif isinstance(m, BatchNorm2d):
            #     m.weight.data.fill_(1)
            #     m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
            )
                # BatchNorm2d(planes * block.expansion),

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        # x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        return x


def resnet45():
    return ResNet(BasicBlock, [3, 4, 6, 6, 3])

def resnet45stride():
    return ResNet(BasicBlock, [3, 4, 6, 6, 3], [(2, 1),1,(2, 1),1,1])
    # return ResNet(BasicBlock, [3, 4, 6, 6, 3], [(2, 2),1,(2, 2),1,1])

def resnet45stride22():
    return ResNet(BasicBlock, [3, 4, 6, 6, 3], [(2, 2),1,(2, 2),1,1])

def network_param(net):
    num_params = 0
    for param in net.parameters():
        num_params += param.numel()
    return num_params / 1e6


if __name__ == '__main__':
    print('test')